Secure AI models
With AI models powering critical decisions, handling sensitive data, and driving business outcomes, how can you ensure they are secure, trustworthy, and protected?
Trust in the age of AI
AI models are rapidly becoming foundational to enterprise operations, powering analytics, automating decisions, and influencing high-impact outcomes. But they also introduce new risks, from tampering and unauthorized access to intellectual property theft and data leakage.
In this new reality, organizations face a critical challenge: how to verify model integrity, protect intellectual property, and ensure models run only in trusted environments. Without this, organizations risk compromised outputs, regulatory exposure, and loss of trust.
To safely deploy AI models, organizations need cryptographic integrity, transparent provenance, and secure execution built into every stage of the model lifecycle.
AI Model Trust: built on integrity, provenance, and secure execution
Verifiable model integrity
Every model is cryptographically signed, hashed, and packaged to ensure it remains untampered and authentic. This provides proof that the model is exactly what it claims to be, from development through deployment.
Transparent provenance
A Model Bill of Materials (MLBOM) creates a complete, traceable record of datasets, dependencies, and transformations, enabling full visibility, auditability, and compliance across the model lifecycle.
Secure runtime execution
Models run within trusted execution environments (TEEs), ensuring data is protected in use and models operate only in verified, secure environments with continuous validation.
How DigiCert helps you protect AI models
AI Model Trust is built on DigiCert’s proven leadership in PKI and intelligent trust. By extending cryptographic integrity, secure key management, and lifecycle governance to AI models, DigiCert enables organizations to protect intellectual property, reduce risk, and confidently deploy AI at scale.
{{anchor:registration}}